Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Measures of entropy and fuzziness related to aggregation operators
Information Sciences—Intelligent Systems: An International Journal
Entropy of fuzzy partitions: a general model
Fuzzy Sets and Systems
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
A reformulation of entropy in the presence of indistinguishability operators
Fuzzy Sets and Systems
Finding fuzzy classification rules using data mining techniques
Pattern Recognition Letters
Short communication: Uncertainty measures for fuzzy relations and their applications
Applied Soft Computing
A note on quality measures for fuzzy association rules
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
The entropy of relations and a new approach for decision tree learning
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Input features' impact on fuzzy decision processes
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Based on different emphases, some general information content measures of fuzzy relations are given. Distinguished from the measures related to fuzzy relations proposed before, these measures try to estimate the information conveyed by multiple-domain fuzzy relations without preassigned probability distribution. Since fuzzy rules can be fully captured by fuzzy relations from an input universe to an output universe, the information content of fuzzy rules can be easily measured by the information content of fuzzy relations proposed by us. However, there also exists a difference between fuzzy relations and fuzzy rules. Rules(especially classification rules) always have a direction from the antecedent and consequent while relations do not have direction. Based on this difference, some general measures for the information content of fuzzy rules are proposed. In practice, these measures can do well in the evaluation and selection of fuzzy rules. Finally, the measures of the information content of fuzzy rules are used to evaluate the stability and sensitivity of fuzzy implication operators in fuzzy control.